La modélisation : un outil pour la gestion et l'aménagement en forêt.
Bibliographic record
Abstract
Pour prédire l'évolution des peuplements forestiers, et ainsi faciliter l'aménagement, les chercheurs ont mis au point différents types de modèles de dynamique forestière.Dans cet article, nous illustrons l'intérêt de ces modèles comme outils pour la gestion. Nous présentons 3 applications : (i) dans un peuplement régulier de chênes, l'utilisation d'un modèle pour prendre en compte l'effet d'un changement de productivité sur la croissance ; (ii) dans un peuplement mélangé irrégulier épicéas - sapins, la comparaison par simulation d'un scénario sylvicole classique et d'un scénario d'éclaircies par trouées ; (iii) à l'échelle d'un massif forestier, la modélisation de l'évolution de la composition de peuplements mélangés. Nous discutons ensuite des limites des modèles ainsi que des perspectives de recherche.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".